Drive Your Business Strategy With Brand Experience Marketing

Brand experience-driven marketing evaluates customers’ perception of your brand and delivers the best solutions in improving the overall brand experience (BX). And so a business strategy that is built on ongoing, credible, and targeted BX research can give your business the longevity that can make you recession-proof.

The way to successfully ace this is by using BX analytics tools built on artificial intelligence and developed specifically for your needs. Insights from these intelligent algorithms can build the right brand perception, increase your brand equity, get better leads, and improve sales conversions. These insights also help you attract the right B2B and B2C customers for more sustainable growth.

In this article, we give you all the scoop on how you can use technology to your advantage for successful brand experience-driven marketing.

Why Is Brand Strategy Important?

A brand is much larger than a product or service. A product may go out of circulation but if you’ve had a super impactful branding strategy, people are going to remember you even after decades of not seeing your product on the shelves. This principle of brand experience marketing covers all industries including essential and non-essential commodities in the B2B and B2C space.

Brands have risen, in fact, from decades of dealing with market disruptions and changing environment and socio-economic conditions by re-strategizing their branding initiatives.

There are numerous ways in which a brand experience-driven strategy can help you. We can encapsulate them as the following -

• Increasing your brand equity
• Creating customers for life
• Improving sales conversions
• Offering brand differentiation
• Boosting brand awareness

How Do Marketers Analyze Brand Experience For Decision-Making?

To develop a strong brand experience marketing plan, marketers need to analyze thousands of customer experience (CX) comments, reviews, survey responses, news articles, and other data. Doing this voice of the customer analysis manually is not possible without errors and so this process is automated using a machine learning (ML) based sentiment analysis model.

This ML tool is first trained with the right training data, results analyzed, and then retrained for optimum efficiency. The entire process comprises key functions like multilingual native natural language processing (NLP), aspect-emotion co-occurrence patterns, granular emotion analysis, sentiment trends, and others, which play their part in customer and brand experience analysis.

Ultimately, the platform automatically recognizes topics and themes in the data, extracts relevant ones, and analyzes each for the sentiment. And in this way provides marketers with whatever insights they need - be it TikTok insights or those from Reddit or any other platform really - so they can put it at the center of their brand experience marketing strategy.

To get a better understanding of how the tool works in conjunction with the data that marketers want it to analyze, here are the steps.

Step1: Data gathering

First, the data is entered into the BX sentiment analysis tool. This can be done directly with the URL of the Google reviews, Youtube video, or any other source. Or, you can upload the data as an excel file if you decide to scrape comments from social media channels like Facebook, TikTok, your CRM tool, etc.

Step 2: Data preparation

The model prepares the data for analysis. It breaks it down into data types, collates it, and then processes it as below.

• Audio/Video - Podcast data and social media videos are transcribed through speech-to-text software. This step is what gets you important brand insights through Instagram social listening as easily as it would from text-based Twitter feeds for brand experience marketing
• Captions - Caption overlays are recognized and their text captured
• Text - All text from comments, reviews, news articles, blogs, and such, is collected and cleaned, removing all non-text data such as punctuations etc. Importantly, the model extracts emojis and hashtags, which helps in eliminating false positives or negatives.

Step 3 - Data analysis

There are various elements to analyzing the brand experience data in this step as detailed below.

• Model training - The ML model is trained with training data meant for the particular industry you are in, such as banking, hospitality, automotive, etc. This dataset is manually labeled when input for training. When the results are obtained, it is compared to the validation dataset. This process is repeated until the model gives the most optimal results.
• Multilingual data analysis - Native language processing is applied through part-of-speech taggers for each language. Using native language models gives more accurate insights than models that use machine translations.
• Custom tagging - Custom tags are made for each aspect and theme found in the customer experience data. This helps the tool automatically segregate text based on these custom tags.
• Topic classification - The topic classifier attaches a theme to a text such as price, food, convenience, etc.
• Sentiment analysis - The text is analyzed for sentiment. The sentiment analysis API analyzes each aspect and theme and assigns the appropriate score. The aggregate of these separate sentiment scores eventually gives the overall sentiment score of your brand.

Step 4. Data visualization

All the insights you need for your brand experience marketing strategy get represented on a customer experience analysis dashboard in easy-to-understand graphs and charts.

Repustate’s BX sentiment analysis platform is built on a very powerful named entity recognition (NER) system. As the name suggests, the capability captures famous named entities in the data such as persons, places, currencies, brands, and such. This gives you a very in-depth view of what customers are talking about when they mention you in their comments.

You can analyze competitor brands, market motivators, and sentiment trends of what customers think of your offers. You can also discover what else they would like from you just as we discovered brand insights from the analysis of a viral McDonald’s video.

In order to do all these different kinds of permutations and combinations, the Repustate platform allows you the flexibility to add or remove aspects, adjust aspect filters, derive insights from aspect-emotion co-occurrences, and set alerts for spikes in social mentions or keywords, and several other advanced features as well.

Channels To Build Your Brand Experience Marketing Strategy

Now that you know of all the insights you can get from an AI-driven platform for your brand experience marketing campaigns, a quick look at all the channels you can use to leverage these brand insights.

1. Website. Use all the brand experience insights you get in improving the user interface (UI) and user experience (UX) of your website.
2. Microsite. Leverage BX marketing insights to develop an engaging microsite for brand imaging and product launches.
3. Enhance customer satisfaction. Improve your customer experience by using the insights to attend to the areas that need attention.
4. Advertising campaigns. Bring fresh ideas to develop a more relatable advertising campaign that is targeted towards your audience profile.
5. Marketing content. You should definitely use the brand experience marketing insights in all your creatives and marketing collaterals such as logo refreshers if necessary, social media content, signages, store decor, television ads, etc.
6. Sponsorships. It’s important to be in touch with the latest that’s happening in the market and industry so that you can choose the right organizations and events to sponsor.
7. Product placements. BX and CX insights can reveal what kind of digital content (television series, YouTube ads, movies, documentaries, etc.) you should use for product placement to drive your business strategy for brand building.
8. Build loyalty programs. Loyalty programs should be aimed at all customer groups in your customer base depending on age, area, cultural nuances, and such. The BX insights you gain from an ML tool will be very useful in this.
9. Promotions. When customers experience a positive feeling in relation to your brand, it promotes brand longevity and customer loyalty. BX insights will tell you how to use this data in your promotions and what avenues you can use for them.
10. Press releases. Sentiment and market trends will tell you when and what kind of press releases you should publish in order to reach out to the right audiences.
11. Social media. Brand experience marketing insights will tell you what your social media persona should be, how you should approach certain audiences, how quick you should be to social mentions and what kind of Influencers you should approach for your brand. Learn more about social media sentiment analysis.
12. Reputation management. Use the BX analytics tool keep closely monitor all channels of communication and marketing so that you are alert for anything that can affect your brand reputation negatively.

Conclusion

Brand management is a very important part of a company’s business strategy because it goes beyond the company’s product. Artificial intelligence tools need not be complicated and that’s what Repustate aims for. The easy-to-use Repustate IQ BX analytics platform offers brand experience marketing insights in 23 languages, at speed and scale, at the touch of a button.

You can use it for YouTube video analysis, analyzing data based on hashtags or certain keywords, and even set alerts for social mentions. Additionally, it lets you have control over your data and increasing needs as your business grows, without having to shell out more on future enhancements or third-party support.